Volume 11, Issue 3, June 2015, Pages 623–632
Asma Karray1, Mohamed Benrejeb2, and Pierre Borne3
1 LARA Automatique, Ecole Nationale d'Ingénieurs de Tunis, Tunisia
2 LA.R.A. Automatique, Ecole Nationale d'Ingénieurs de Tunis, BP37, le Belvédère, 1002 Tunis, Tunisia
3 LAGIS, Ecole Centrale de Lille, Villeneuve d'Ascq, France
Original language: English
Copyright © 2015 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper deals with the multi-objective single-machine scheduling problem in agro-food industry. To solve this problem, a new hybrid algorithm is proposed. This new algorithm named SHGA/SA is composed of two well-known metaheuristics: genetic algorithms and simulated annealing. The results show that our new approach can be used to solve the single-machine scheduling problem efficiently and in a short computational time. Also, the results show that the hybrid algorithm outperforms both the GA and SA.
Author Keywords: Hybridization, genetic algorithms, simulated annealing, single-machine scheduling problem, agro-food industry.
Asma Karray1, Mohamed Benrejeb2, and Pierre Borne3
1 LARA Automatique, Ecole Nationale d'Ingénieurs de Tunis, Tunisia
2 LA.R.A. Automatique, Ecole Nationale d'Ingénieurs de Tunis, BP37, le Belvédère, 1002 Tunis, Tunisia
3 LAGIS, Ecole Centrale de Lille, Villeneuve d'Ascq, France
Original language: English
Copyright © 2015 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This paper deals with the multi-objective single-machine scheduling problem in agro-food industry. To solve this problem, a new hybrid algorithm is proposed. This new algorithm named SHGA/SA is composed of two well-known metaheuristics: genetic algorithms and simulated annealing. The results show that our new approach can be used to solve the single-machine scheduling problem efficiently and in a short computational time. Also, the results show that the hybrid algorithm outperforms both the GA and SA.
Author Keywords: Hybridization, genetic algorithms, simulated annealing, single-machine scheduling problem, agro-food industry.
How to Cite this Article
Asma Karray, Mohamed Benrejeb, and Pierre Borne, “A hybrid algorithm to solve the single-machine scheduling problem,” International Journal of Innovation and Applied Studies, vol. 11, no. 3, pp. 623–632, June 2015.
